{"id":"W4413169675","doi":"10.1093/jbmrpl/ziaf128","title":"Transcriptomic and lipidomic profiling provide novel insight into the pathogenesis of monogenic <i>SGMS2</i> -related osteoporosis","year":2025,"lang":"en","type":"article","venue":"JBMR Plus","topic":"Caveolin-1 and cellular processes","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine","funders":"Biocenter Finland; Samfundet Folkhälsan; Novo Nordisk; Magnus Ehrnroothin Säätiö; Novo Nordisk Fonden; Academy of Finland; Lastentautien Tutkimussäätiö; Folkhälsanin Tutkimussäätiö; Sigrid Juséliuksen Säätiö; China Scholarship Council; Suomen Lääketieteen Säätiö; Korvatautien Tutkimussäätiö; Helsingin Yliopisto","keywords":"Profiling (computer programming); Transcriptome; Osteoporosis; Computational biology; Pathogenesis; Bioinformatics; Medicine; Biology; Genetics; Computer science; Gene; Pathology; Gene expression","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001446642,0.0004102822,0.0003234772,0.0007724334,0.0001790486,0.0003590937,0.0001036806,0.0003369417,0.0008777099],"category_scores_gemma":[0.0001610441,0.0001269091,0.0003190807,0.0004350433,0.0001616281,0.0001440732,0.0002191974,0.0002611363,0.0002434709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001473032,"about_ca_system_score_gemma":0.0001273387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004868284,"about_ca_topic_score_gemma":0.0006818495,"domain_scores_codex":[0.9998797,0.00001562749,0.00001006795,0.00004042823,0.00003383534,0.00002036445],"domain_scores_gemma":[0.9999229,0.00001266077,0.00003035974,0.000006247812,0.00001409315,0.00001370318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005180652,0.00001846862,0.02514673,0.00009036049,0.00005528717,0.0003816525,0.00004701397,0.00008522545,0.9686264,0.00006577089,0.000130823,0.004834025],"study_design_scores_gemma":[0.00004354382,0.0005650362,0.7479153,0.00007019167,0.0003658113,0.004265115,0.0005531095,0.00354728,0.2361351,0.0008931585,0.005614106,0.00003228181],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895172,0.002263383,0.00308684,0.0001666651,0.00002026406,0.00003448769,0.0041085,0.00007912958,0.0007236082],"genre_scores_gemma":[0.9910983,0.001251443,0.003134629,0.0001814642,0.00002001757,0.00003895599,0.003176742,0.00001790847,0.001080574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008777099,"threshold_uncertainty_score":0.002936244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00517528028090147,"score_gpt":0.2162836530472244,"score_spread":0.2111083727663229,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}